Google Exam Professional Data Engineer Topic 3 Question 71 Discussion

Actual exam question for Google's Professional Data Engineer exam

Question #: 71
Topic #: 3

[All Professional Data Engineer Questions]

You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of dat

a. What should you do?

ABuild and train a complex classification model with Spark MLlib to generate labels and filter the results.
Deploy the models using Cloud Dataproc. Call the model from your application.

BBuild and train a classification model with Spark MLlib to generate labels. Build and train a second
classification model with Spark MLlib to filter results to match customer preferences. Deploy the models
using Cloud Dataproc. Call the models from your application.

CBuild an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.

DBuild an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.

Show Suggested Answer

Suggested Answer: C

by Lucy at Oct 09, 2023, 05:21 AM

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11 days ago

I think option A is the best choice. Using Spark MLlib for classification and Cloud Dataproc for deployment seems efficient.

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